style: remove herald sentences (announcer claudisms)

Nine sentences of the 'The diagnosis comes first.' genus removed or
folded — announcements that add nothing over the sentence that follows:
'This population also reproduces.', 'One consequence of drift deserves
its genetic name.', 'It is also only the entry point.' (folded), 'The
question this paper addresses is...', 'I begin with a result...', 'The
engineering point survives the definition:' (rewritten plain), 'The
supported conclusion, stated conditionally:', 'The strongest constraint
comes from...' (inverted to content-first), 'The framework's reading of
that trajectory is concrete.'. Functional enumerator colons and content-
bearing topic sentences kept. 19 pp rebuild clean.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

paper/pnas/main.md

@@ -47,8 +47,8 @@ millions of models (Hugging Face alone grew past three million by 2026), and the
 independent creations: the overwhelming majority are fine-tunes, distillations, or merges of a small
 number of foundation models, forming large family trees whose lineage structure, inherited traits,
 and mutation dynamics are already being mapped with explicitly phylogenetic methods (1–3).
-This population also reproduces. Weight-space *model merging*, the direct combination of trained
-parents into a new model, is mainstream community practice with standard tooling and thousands of
+Weight-space *model merging*, the direct combination of trained parents into a new model, is
+mainstream community practice with standard tooling and thousands of
 hybrid checkpoints, including leaderboard-topping ones (4–7), and the engineering literature
 describes it in evolutionary vocabulary: "crossover," "mutation," "mate choice," populations of
 merging models that climb benchmarks (5, 8–10).
@@ -66,7 +66,7 @@ transfer the quantitative framework of the branch of biology built for exactly t
 population genetics of the evolution of sex, and use it to treat multigenerational model populations
 as systems whose inheritance, diversity, and compatibility can be measured, predicted, and managed.
 
-The diagnosis comes first. Training each generation of a model on the previous generation's output
+Training each generation of a model on the previous generation's output
 degrades it (*model collapse*): rare capabilities vanish first, and the lineage drifts toward its own
 most common behaviour (21). That degradation is, mathematically, *genetic drift*, the loss of rare
 variants that any finite population suffers when each generation is a finite sample of the last. The
@@ -74,15 +74,14 @@ identification has been made repeatedly and independently: for sequential infere
 learning (22), for language-model text ecosystems (23), as a closed-form first-extinction law placing
 collapse onset at the Wright–Fisher first-extinction time (24), and in quantitative-genetic form for
 self-consuming diffusion models (25). A diagnosis reached so often, from such different starting
-points, marks population genetics as the natural mathematics of the setting. It is also only the entry
-point. Population genetics is not, at heart, a theory of decay; it is a theory of the mechanisms that
+points, marks population genetics as the natural mathematics of the setting, though only as its entry
+point: population genetics is not, at heart, a theory of decay; it is a theory of the mechanisms that
 maintain and build populations despite decay (immigration, recombination, selection, population
 structure) and of where those mechanisms reach their limits. This paper develops that fuller structure
 for model populations: the arc from drift through its remedies to its limit, reproductive isolation,
 carried as one framework from closed forms to trained networks to language models.
 
-The question this paper addresses is what to do with that diagnosis. An operator of a model
-population faces recurring decisions for which there is no principled guidance: how much verified
+An operator of a model population faces recurring decisions for which there is no principled guidance: how much verified
 real data does retraining need before a lineage decays; will combining two particular models compose
 their abilities or damage them; can incompatibility be detected before paying for a failed merge; and
 when should specialists be kept separate rather than consolidated? In practice these are settled by
@@ -113,8 +112,7 @@ refit) reproduces both. Throughout, a real learner is therefore treated as Wrigh
 estimator bias*, and the drift signs (rare-first loss; the grounding response)
 survived that bias in every architecture I tested, including a convolutional VAE retrained on its own
 generated digits, where the dry lineage collapses to a single blurred digit class while 10% grounding
-holds all thirty modes (Fig. 1). One consequence of drift deserves its genetic name. Retraining on a
-single parent is *asexual reproduction*, and sustained loss under it carries the defining consequence
+holds all thirty modes (Fig. 1). Retraining on a single parent is *asexual reproduction*, and sustained loss under it carries the defining consequence
 of *Muller's ratchet* (28): once every copy of a rare capability is gone from all parents and sources,
 no recombination can rebuild it, so remedies must act before fixation-by-loss (a consequence-level
 correspondence; the minimal model lacks the ratchet's recurrent-mutation driver).
@@ -146,8 +144,8 @@ In the minimal model, grounding from a fixed real source is *immigration* into a
 practical number is an operational threshold, and I define it as such: under the tested population
 size and Zipf source distribution, `g ≈ 0.05` retained most (≥95%) of equilibrium diversity
 indefinitely, with the required fraction depending on sample size, source distribution, and the
-chosen retention target (dependencies in SI). The engineering point survives the definition: verified
-real data is cheap insurance at fractions far below one. But the same analysis yields a floor the field's
+chosen retention target (dependencies in SI). Verified real data remains, on any of these
+definitions, cheap insurance at fractions far below one. But the same analysis yields a floor the field's
 average-loss framing misses: under unstratified sampling from the source, a capability of rarity `p`
 appears in a real-data batch of size `m` with probability `1 − e^{−m·p}`, so `m·p ≈ 1` marks roughly a
 63% chance of one example per batch: a soft observation floor, with higher confidence priced
@@ -166,8 +164,7 @@ fraction, the measured price of the estimator bias).
 
 ### Recombination: a conservation law, its operators, and offspring that exceed every parent
 
-The largest returns from the transfer concern merging. I begin with a result about the most common
-operator, stated with its assumptions. **Proposition (blending inheritance, rare-item
+The largest returns from the transfer concern merging. **Proposition (blending inheritance, rare-item
 regime).** Let K parents independently retain a rare item (mass `p` when retained), and let the child
 draw `n` samples either from one parent chosen at random or from the *mean of the parents' output
 distributions*. Expected item mass is identical under the two schemes; and in the rare-item regime
@@ -265,7 +262,7 @@ information-theoretic (no single model can satisfy contradictory conventions; SI
 Proposition S2), with the framework's role being the *structure around it*: which divergences
 generate conflict, and what moves the cliff.
 
-The strongest constraint comes from the pre-registered *emergent test*: true BDM incompatibilities are
+The pre-registered *emergent test* constrains the claim most: true BDM incompatibilities are
 emergent (each lineage's changes harmless alone), so I let children diverge with *no conflicting
 signal anywhere*, using complementary class specialists and divergent input conventions, to 6.4× the base
 training. No isolation emerged (residual 0.000 throughout); instead the merge *rescued* the two
@@ -298,7 +295,7 @@ baseline. The pre-registered outcome is the merge penalty against oracle parent
 hybrid-load analogue), also reported against best- and mean-parent references because the predictor
 ordering is sensitive to that choice.
 
-The supported conclusion, stated conditionally: across this controlled grid, pre-merge functional
+Across this controlled grid, pre-merge functional
 disagreement predicted merge penalties (clustered bootstrap CIs excluding zero; held-out
 leave-one-condition-out ρ ≈ 0.35–0.40), whereas LoRA-delta cosine and L2 showed no statistically
 detectable association; gradient alignment carried intermediate signal. Head-to-head predictor
@@ -417,7 +414,7 @@ an evolving population, is also a forecast about where these results matter next
 development is consolidating around exactly the operators studied here: synthetic-data flywheels
 (inheritance), merging and routing of specialist fine-tunes (recombination and population structure),
 verifier-gated data pipelines (grounded selection), and periodic consolidation of adapters into new
-bases. The framework's reading of that trajectory is concrete. If coming model generations remain what
+bases. If coming model generations remain what
 the tested regimes found, freely recombinable in the absence of conflicting conventions, then the
 ecosystem evolves as one interbreeding population, and the levers that matter are grounding budgets
 priced per rare capability and diversity preserved deliberately. If instead long-horizon